library(tidyverse)
library(fs)
ngsadmix_dir <- "data/ngsadmix_all/maf_0.05" # set NGSadmix outputs directory
N_K <- 10 # set number of K run
N_reps <- 4 # set number of reps run
# pull all log files
log_files <- list.files(ngsadmix_dir, pattern = ".log", full.names = T, recursive=T)
# read in all logs
all_logs <- lapply(1:length(log_files), FUN = function(i) readLines(log_files[i]))
# make list of the line that starts with "best like=" from all logs, just target 'b'
library(stringr)
bestlikes_str_list <- sapply(1:length(log_files), FUN= function(x) all_logs[[x]][which(str_sub(all_logs[[x]], 1, 1) == 'b')])
# make dataframe with 1:N_K and N_reps to add likelihood values
loglikes <- data.frame(K = rep(2:N_K, each=N_reps))
# add the log likelihood (first number in the string)
loglikes$loglike<-as.vector(as.numeric( sub("\\D*(\\d+).*", "\\1", bestlikes_str_list) ))
tapply(loglikes$loglike, loglikes$K, FUN= function(x) mean(abs(x))/sd(abs(x)))
2 3 4 5 6 7 8 9 10
1.314224e+03 3.724947e+08 Inf Inf 5.753390e+02 6.269528e+03 1.961828e+04 Inf Inf
alewife_palette <- c("#636c62", "#869ca8", "#a2878a", "#b6bc9f", "#ecc2a3", "#eceeed", "#242b35", "#a39faa", "#ac9b7c")
metadata <- read_csv("data/herring_metadata.csv") %>%
select(NMFS_DNA_ID,
GENUS,
SPECIES,
STATE_F,
WATERSHED,
WATER_NAME,
grouping_v3)
Rows: 94 Columns: 12── Column specification ───────────────────────────────────
Delimiter: ","
chr (11): sample, NMFS_DNA_ID, BOX_ID, BOX_POSITION, SA...
dbl (1): BATCH_ID
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
gord <- c("BLUE",
"HYBR",
"GRTL",
"FINL",
"MIDL",
"CONL",
"NATLA",
"MIDA")
word <- c("Petitcodiac River",
"Hudson River",
"Lake Yonah",
"Lake Hartwell",
"Altamaha River",
"Roanoke River",
"Lake Superior",
"Lake Michigan",
"Lake Ontario",
"Canandaigua Lake",
"Cayuga Lake",
"Seneca Lake",
"Otisco Lake",
"East Grand Lake",
"Lake Champlain",
"Pattagansett Lake",
"Rogers Lake",
"Quonnipaug Lake",
"Miramichi River",
"Saco River",
"Black Creek",
"Choptank River")
labels <- read_tsv("data/admix-labels.tsv")
Rows: 23 Columns: 4── Column specification ───────────────────────────────────
Delimiter: "\t"
chr (4): grouping_v3, group_label, WATER_NAME, pop_label
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
tmp <- metadata %>%
mutate(xpos = 1:n())
group_pos <- tmp %>%
group_by(grouping_v3) %>%
summarise(midx = (min(xpos) - 0.5 + max(xpos) + 0.5)/ 2) %>%
mutate(midy = 1)
group_labels <- group_pos %>%
left_join(labels %>% group_by(grouping_v3) %>% slice(1))
Joining with `by = join_by(grouping_v3)`
K_2_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_2_rep_1/output.qopt_with_sample_names",
header = FALSE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
Error in `inner_join()`:
! Join columns in `x` must be present in the data.
✖ Problem with `sample`.
Backtrace:
1. ... %>% arrange(gfact, wfact)
5. dplyr:::inner_join.data.frame(., metadata, by = c(sample = "NMFS_DNA_ID"))
K_3_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_3_rep_1/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_3_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_3_rep_2/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_3_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_3_rep_3/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_3_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_3_rep_4/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
barplot(t(K_3_rep_1[2:4]),
col = alewife_palette,
names = K_2_rep_1$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 2, rep 1",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_3_rep_2[2:4]),
col = alewife_palette,
names = K_2_rep_2$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 2, rep 2",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_3_rep_3[2:4]),
col = alewife_palette,
names = K_2_rep_3$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 2, rep 3",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_3_rep_4[2:4]),
col = alewife_palette,
names = K_2_rep_4$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 2, rep 4",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

K_4_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_4_rep_1/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_4_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_4_rep_2/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_4_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_4_rep_3/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_4_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_4_rep_4/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
barplot(t(K_4_rep_1[2:5]),
col = alewife_palette,
names = K_4_rep_1$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 4, rep 1",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_4_rep_2[2:5]),
col = alewife_palette,
names = K_4_rep_2$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 4, rep 2",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_4_rep_3[2:5]),
col = alewife_palette,
names = K_4_rep_3$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 4, rep 3",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_4_rep_4[2:5]),
col = alewife_palette,
names = K_4_rep_4$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 4, rep 4",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

K_5_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_5_rep_1/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_5_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_5_rep_2/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_5_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_5_rep_3/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_5_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_5_rep_4/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
barplot(t(K_5_rep_1[2:6]),
col = alewife_palette,
names = K_5_rep_1$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 5, rep 1",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_5_rep_2[2:6]),
col = alewife_palette,
names = K_5_rep_2$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 5, rep 2",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_5_rep_3[2:6]),
col = alewife_palette,
names = K_5_rep_3$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 5, rep 3",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_5_rep_4[2:6]),
col = alewife_palette,
names = K_5_rep_4$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 5, rep 4",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

K_6_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_6_rep_1/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_6_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_6_rep_2/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_6_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_6_rep_3/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_6_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_6_rep_4/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
barplot(t(K_6_rep_1[2:7]),
col = alewife_palette,
names = K_6_rep_1$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 6, rep 1",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_6_rep_2[2:7]),
col = alewife_palette,
names = K_6_rep_2$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 6, rep 2",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_6_rep_3[2:7]),
col = alewife_palette,
names = K_6_rep_3$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 6, rep 3",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_6_rep_4[2:7]),
col = alewife_palette,
names = K_6_rep_4$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 6, rep 4",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

K_7_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_7_rep_1/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_7_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_7_rep_2/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_7_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_7_rep_3/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_7_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_7_rep_4/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
barplot(t(K_7_rep_1[2:8]),
col = alewife_palette,
names = K_7_rep_1$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 7, rep 1",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_7_rep_2[2:8]),
col = alewife_palette,
names = K_7_rep_2$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 7, rep 2",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_7_rep_3[2:8]),
col = alewife_palette,
names = K_7_rep_3$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 7, rep 3",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_7_rep_4[2:8]),
col = alewife_palette,
names = K_7_rep_4$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 7, rep 4",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

K_8_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_8_rep_1/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_8_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_8_rep_2/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_8_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_8_rep_3/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_8_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_8_rep_4/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
barplot(t(K_8_rep_1[2:9]),
col = alewife_palette,
names = K_8_rep_1$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 8, rep 1",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_8_rep_2[2:9]),
col = alewife_palette,
names = K_8_rep_2$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 8, rep 2",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_8_rep_3[2:9]),
col = alewife_palette,
names = K_8_rep_3$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 8, rep 3",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_8_rep_4[2:9]),
col = alewife_palette,
names = K_8_rep_4$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 8, rep 4",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

K_9_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_9_rep_1/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_9_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_9_rep_2/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_9_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_9_rep_3/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_9_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_9_rep_4/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
barplot(t(K_9_rep_1[2:10]),
col = alewife_palette,
names = K_9_rep_1$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 9, rep 1",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_9_rep_2[2:10]),
col = alewife_palette,
names = K_9_rep_2$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 9, rep 2",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_9_rep_3[2:10]),
col = alewife_palette,
names = K_9_rep_3$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 9, rep 3",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_9_rep_4[2:10]),
col = alewife_palette,
names = K_9_rep_4$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 9, rep 4",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

K_10_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_10_rep_1/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_10_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_10_rep_2/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_10_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_10_rep_3/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
K_10_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_10_rep_4/output.qopt_with_sample_names",
header = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID")) %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)
barplot(t(K_10_rep_1[2:11]),
col = alewife_palette,
names = K_10_rep_1$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 10, rep 1",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_10_rep_2[2:11]),
col = alewife_palette,
names = K_10_rep_2$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 10, rep 2",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_10_rep_3[2:11]),
col = alewife_palette,
names = K_10_rep_3$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 10, rep 3",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

barplot(t(K_10_rep_4[2:11]),
col = alewife_palette,
names = K_10_rep_4$WATER_NAME,
las = 2,
cex.names = 0.5,
main = "K 10, rep 4",
space = 0,
border = "white",
cex.axis = 0.75,
cex.lab = 1,
xpd = NA)

ngsadmix_files <- dir_ls("data/ngsadmix_all",
recurse = TRUE,
glob = "*.qopt_with_sample_names")
ngsAdmix_tib <- lapply(ngsadmix_files, function(x) {
read.table(x,
header = TRUE) %>%
pivot_longer(cols = -sample,
names_to = "Qval",
values_to = "value") %>%
mutate(path = x,
.before = sample)
}) %>%
bind_rows() %>%
filter(!is.na(value)) %>%
mutate(Qval = str_replace(Qval,
"X",
"Q")) %>%
extract(path,
into = c("K",
"rep"),
regex = ".*K_([0-9]+)_rep_([0-9]+)/.*$",
convert = TRUE) %>%
inner_join(.,
metadata,
by = c("sample" = "NMFS_DNA_ID"),
relationship = "many-to-many") %>%
mutate(gfact = factor(grouping_v3,
levels = gord),
wfact = factor(WATER_NAME,
levels = word)) %>%
arrange(gfact, wfact)

---
title: "Admixture Proportions"
subtitle: "All Samples"
author: "Hana K. Moyle"
date: "2024 July 23"
output: html_notebook
---

```{r libraries}
library(tidyverse)
library(fs)
```

```{r log_likelihoods}
ngsadmix_dir <- "data/ngsadmix_all/maf_0.05" # set NGSadmix outputs directory
N_K <- 10    # set number of K run
N_reps <- 4  # set number of reps run

# pull all log files
log_files <- list.files(ngsadmix_dir, pattern = ".log", full.names = T, recursive=T)

# read in all logs
all_logs <- lapply(1:length(log_files), FUN = function(i) readLines(log_files[i]))

# make list of the line that starts with "best like=" from all logs, just target 'b'
library(stringr)
bestlikes_str_list <- sapply(1:length(log_files), FUN= function(x) all_logs[[x]][which(str_sub(all_logs[[x]], 1, 1) == 'b')])

# make dataframe with 1:N_K and N_reps to add likelihood values
loglikes <- data.frame(K = rep(2:N_K, each=N_reps))

# add the log likelihood (first number in the string)
loglikes$loglike<-as.vector(as.numeric( sub("\\D*(\\d+).*", "\\1", bestlikes_str_list) ))

tapply(loglikes$loglike, loglikes$K, FUN= function(x) mean(abs(x))/sd(abs(x)))
```

```{r data_org}
alewife_palette <- c("#636c62", "#869ca8", "#a2878a", "#b6bc9f", "#ecc2a3", "#eceeed", "#242b35", "#a39faa", "#ac9b7c", "#3b3a35")

metadata <- read_csv("data/herring_metadata.csv") %>%
  select(NMFS_DNA_ID, 
         GENUS, 
         SPECIES, 
         STATE_F, 
         WATERSHED, 
         WATER_NAME, 
         grouping_v3)
gord <- c("BLUE", 
           "HYBR", 
           "GRTL", 
           "FINL", 
           "MIDL", 
           "CONL", 
           "NATLA", 
           "MIDA")
word <- c("Petitcodiac River", 
           "Hudson River", 
           "Lake Yonah", 
           "Lake Hartwell", 
           "Altamaha River", 
           "Roanoke River", 
           "Lake Superior", 
           "Lake Michigan", 
           "Lake Ontario", 
           "Canandaigua Lake", 
           "Cayuga Lake", 
           "Seneca Lake", 
           "Otisco Lake", 
           "East Grand Lake", 
           "Lake Champlain", 
           "Pattagansett Lake", 
           "Rogers Lake", 
           "Quonnipaug Lake", 
           "Miramichi River", 
           "Saco River", 
           "Black Creek", 
           "Choptank River")

labels <- read_tsv("data/admix-labels.tsv") 

tmp <- metadata %>%
  mutate(xpos = 1:n())

group_pos <- tmp %>%
  group_by(grouping_v3) %>%
  summarise(midx = (min(xpos) - 0.5 + max(xpos) + 0.5)/ 2) %>%
  mutate(midy = 1)

group_labels <- group_pos %>%
  left_join(labels %>% group_by(grouping_v3) %>% slice(1))
```

```{r K2}
K_2_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_2_rep_1/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  pivot_longer(cols = -sample, 
               names_to = "Qval", 
               values_to = "value")
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_2_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_2_rep_2/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_2_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_2_rep_3/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_2_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_2_rep_4/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

barplot(t(K_2_rep_1[2:3]), 
        col = alewife_palette, 
        names = K_2_rep_1$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 2, rep 1", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_2_rep_2[2:3]), 
        col = alewife_palette, 
        names = K_2_rep_2$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 2, rep 2", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_2_rep_3[2:3]), 
        col = alewife_palette, 
        names = K_2_rep_3$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 2, rep 3", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_2_rep_4[2:3]), 
        col = alewife_palette, 
        names = K_2_rep_4$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 2, rep 4", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

```

```{r K3}
K_3_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_3_rep_1/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_3_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_3_rep_2/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_3_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_3_rep_3/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_3_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_3_rep_4/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

barplot(t(K_3_rep_1[2:4]), 
        col = alewife_palette, 
        names = K_3_rep_1$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 2, rep 1", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_3_rep_2[2:4]), 
        col = alewife_palette, 
        names = K_3_rep_2$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 2, rep 2", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_3_rep_3[2:4]), 
        col = alewife_palette, 
        names = K_3_rep_3$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 2, rep 3", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_3_rep_4[2:4]), 
        col = alewife_palette, 
        names = K_3_rep_4$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 2, rep 4", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)
```

```{r K4}
K_4_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_4_rep_1/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_4_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_4_rep_2/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_4_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_4_rep_3/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_4_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_4_rep_4/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

barplot(t(K_4_rep_1[2:5]), 
        col = alewife_palette, 
        names = K_4_rep_1$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 4, rep 1", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_4_rep_2[2:5]), 
        col = alewife_palette, 
        names = K_4_rep_2$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 4, rep 2", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_4_rep_3[2:5]), 
        col = alewife_palette, 
        names = K_4_rep_3$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 4, rep 3", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_4_rep_4[2:5]), 
        col = alewife_palette, 
        names = K_4_rep_4$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 4, rep 4", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)
```

```{r K5}
K_5_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_5_rep_1/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_5_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_5_rep_2/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_5_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_5_rep_3/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_5_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_5_rep_4/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

barplot(t(K_5_rep_1[2:6]), 
        col = alewife_palette, 
        names = K_5_rep_1$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 5, rep 1", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_5_rep_2[2:6]), 
        col = alewife_palette, 
        names = K_5_rep_2$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 5, rep 2", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_5_rep_3[2:6]), 
        col = alewife_palette, 
        names = K_5_rep_3$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 5, rep 3", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_5_rep_4[2:6]), 
        col = alewife_palette, 
        names = K_5_rep_4$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 5, rep 4", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)
```

```{r K6}
K_6_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_6_rep_1/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_6_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_6_rep_2/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_6_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_6_rep_3/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_6_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_6_rep_4/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

barplot(t(K_6_rep_1[2:7]), 
        col = alewife_palette, 
        names = K_6_rep_1$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 6, rep 1", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_6_rep_2[2:7]), 
        col = alewife_palette, 
        names = K_6_rep_2$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 6, rep 2", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_6_rep_3[2:7]), 
        col = alewife_palette, 
        names = K_6_rep_3$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 6, rep 3", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_6_rep_4[2:7]), 
        col = alewife_palette, 
        names = K_6_rep_4$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 6, rep 4", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)
```

```{r K7}
K_7_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_7_rep_1/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_7_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_7_rep_2/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_7_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_7_rep_3/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_7_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_7_rep_4/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

barplot(t(K_7_rep_1[2:8]), 
        col = alewife_palette, 
        names = K_7_rep_1$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 7, rep 1", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_7_rep_2[2:8]), 
        col = alewife_palette, 
        names = K_7_rep_2$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 7, rep 2", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_7_rep_3[2:8]), 
        col = alewife_palette, 
        names = K_7_rep_3$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 7, rep 3", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_7_rep_4[2:8]), 
        col = alewife_palette, 
        names = K_7_rep_4$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 7, rep 4", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)
```

```{r K8}
K_8_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_8_rep_1/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_8_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_8_rep_2/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_8_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_8_rep_3/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_8_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_8_rep_4/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

barplot(t(K_8_rep_1[2:9]), 
        col = alewife_palette, 
        names = K_8_rep_1$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 8, rep 1", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_8_rep_2[2:9]), 
        col = alewife_palette, 
        names = K_8_rep_2$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 8, rep 2", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_8_rep_3[2:9]), 
        col = alewife_palette, 
        names = K_8_rep_3$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 8, rep 3", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_8_rep_4[2:9]), 
        col = alewife_palette, 
        names = K_8_rep_4$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 8, rep 4", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)
```

```{r K9}
K_9_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_9_rep_1/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_9_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_9_rep_2/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_9_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_9_rep_3/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_9_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_9_rep_4/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

barplot(t(K_9_rep_1[2:10]), 
        col = alewife_palette, 
        names = K_9_rep_1$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 9, rep 1", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_9_rep_2[2:10]), 
        col = alewife_palette, 
        names = K_9_rep_2$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 9, rep 2", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_9_rep_3[2:10]), 
        col = alewife_palette, 
        names = K_9_rep_3$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 9, rep 3", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_9_rep_4[2:10]), 
        col = alewife_palette, 
        names = K_9_rep_4$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 9, rep 4", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)
```

```{r K10}
K_10_rep_1 <- read.table("data/ngsadmix_all/maf_0.05/K_10_rep_1/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_10_rep_2 <- read.table("data/ngsadmix_all/maf_0.05/K_10_rep_2/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_10_rep_3 <- read.table("data/ngsadmix_all/maf_0.05/K_10_rep_3/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

K_10_rep_4 <- read.table("data/ngsadmix_all/maf_0.05/K_10_rep_4/output.qopt_with_sample_names", 
                        header = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID")) %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)

barplot(t(K_10_rep_1[2:11]), 
        col = alewife_palette, 
        names = K_10_rep_1$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 10, rep 1", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_10_rep_2[2:11]), 
        col = alewife_palette, 
        names = K_10_rep_2$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 10, rep 2", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_10_rep_3[2:11]), 
        col = alewife_palette, 
        names = K_10_rep_3$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 10, rep 3", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)

barplot(t(K_10_rep_4[2:11]), 
        col = alewife_palette, 
        names = K_10_rep_4$WATER_NAME, 
        las = 2, 
        cex.names = 0.5, 
        main = "K 10, rep 4", 
        space = 0, 
        border = "white", 
        cex.axis = 0.75, 
        cex.lab = 1, 
        xpd = NA)
```

```{r}
ngsadmix_files <- dir_ls("data/ngsadmix_all", 
                         recurse = TRUE, 
                         glob = "*.qopt_with_sample_names")

ngsAdmix_tib <- lapply(ngsadmix_files, function(x) {
  read.table(x, 
             header = TRUE) %>%
    pivot_longer(cols = -sample, 
                 names_to = "Qval", 
                 values_to = "value") %>%
    mutate(path = x, 
           .before = sample)
}) %>%
  bind_rows() %>%
  filter(!is.na(value)) %>%
  mutate(Qval = str_replace(Qval, 
                            "X", 
                            "Q")) %>%
  extract(path, 
          into = c("K", 
                   "rep"), 
          regex = ".*K_([0-9]+)_rep_([0-9]+)/.*$", 
          convert = TRUE) %>%
  inner_join(., 
             metadata, 
             by = c("sample" = "NMFS_DNA_ID"), 
             relationship = "many-to-many") %>%
  mutate(gfact = factor(grouping_v3, 
                        levels = gord), 
         wfact = factor(WATER_NAME, 
                        levels = word)) %>%
  arrange(gfact, wfact)
```

```{r}
ggplot(ngsAdmix_tib) +
  geom_col(mapping = aes(x = sample, 
                         y = value, 
                         fill = Qval)) +
  scale_fill_manual(values = alewife_palette) + 
  scale_x_discrete(label = metadata$WATER_NAME) +
  theme(axis.text.x = element_text(angle = 90, 
                                   size = 8, 
                                   vjust = 0.5)) +
  facet_grid(K ~ rep)
```

